4.89
(9 Ratings)

Foundational Course in Data Analytics with Python Programming Language

Categories: Foundational Courses
Wishlist Share

Course Prerequisite(s)

About Course

Are you ready to unlock the power of data? In today’s world, the ability to understand, analyze, and make sense of information is key to success in virtually every industry. This comprehensive, hands-on course is your perfect starting point to master the foundational Python skills essential for a thriving career in data science and beyond.

This program transforms you from a Python novice into a confident data explorer. You’ll move beyond just writing code; you’ll learn to truly understand and manipulate data, preparing you for real-world challenges.

What Will You Learn?

  • 1. Understanding Python's Core Syntax
  • 2. Define and Utilize Variables
  • 3. Identify and Apply Data Types
  • 4. Implement Conditional Logic
  • 5. Develop for Loops for Iteration
  • 6. Construct while Loops for Conditional Repetition
  • 7. Understand the pass Statement
  • 8. Master Jupyter Notebook Cells: Differentiate between and effectively use Code cells for Python execution and Markdown cells for rich text documentation within notebooks.
  • 9. Import Data from Excel Files: Confidently use the Pandas library to read and load data from .xlsx or .xls Excel files into DataFrame structures.
  • 10. Select Data by Column and Row Labels
  • 11. Identify Missing Values: Efficiently detect and count missing (NaN) values across their DataFrame or within specific columns.
  • 12. Apply Missing Value Imputation Techniques
  • 13. Handle Duplicate Data: Identify and remove duplicate rows from a DataFrame, ensuring data uniqueness and integrity.
  • 14. Correct Data Types: Convert columns to appropriate data types (e.g., strings to numbers, or strings to datetime objects) to ensure data validity and enable proper analysis.
  • 15. Calculate and Interpret Measures of Central Tendency: Compute and explain the mean and median of a dataset, understanding what each measure reveals about the data's central point.
  • 16. Execute and Interpret the Chi-Square Test of Independence: Conduct a Chi-Square Test of Independence to assess whether there is a statistically significant relationship between two categorical variables, and interpret the p-value to draw conclusions about their dependency.
  • 17. Introduction to Data Visualization
  • 18. Manipulating Data with Numpy Function
  • 19. Introduction to Public Sentiment Analysis with Wordcloud
  • 20. Filelink Creation in Python

Course Content

Learning Objectives:
Upon completion of this course, students will be able to: Set up and effectively use the Jupyter Notebook environment for Python coding. Write and execute basic Python code cells in a Jupyter Notebook. Understand and implement conditional logic using if, elif, and else statements. Control program flow using for and while loops for repetitive tasks. Understand the purpose and practical application of the pass statement. Combine control flow statements to solve simple programming problems.

  • Lesson 1, Introduction: Why You Should Learn Python
    00:00
  • Lesson 2: Video Lecture with Notes on Career Opportunities in Data Science
    34:02
  • Lesson 3: What is Python?
  • Lesson 4, Practical Class 1 with Note: Code with me in the Cloud
    37:08
  • Lesson 5: Overview of Python Syntax and Indentation
  • Lesson 6: Data Types and Variables (Strings, Integers, Floats, Booleans)
  • Lesson 7, Practical Class 2 with Note: Conditional Statements in Python
    31:52
  • Lesson 8, Practical Class 3: Solution to Assignment
    15:28
  • Lesson 9, Practical Class 4 with Note in Loop Statements
    43:05
  • Lesson 10, Practical Class 5: Solution to Class Assignment on Loop
    44:03
  • Lesson 11, Practical Class 6: Solution to Qualifying Exam in Appetizer Course
    16:40
  • Lesson 12, Practical Class 7 with Note- pass Statement
    14:55
  • Lesson 13, Practical Class 8: Solution to Class Assignment on Pass Statement
    07:03
  • Download Dataset for Your Analysis Here
  • Lesson 14, Practical Class 9: Making Filelink, Filtering, Selecting and Dataframe
    44:33
  • Lesson 15, Practical Class 10: Descriptive Analysis 1
    15:21
  • Lesson 16: Practical Quiz 1
  • Lesson 17: Practical Class 11 with Note: Numpy 1
    44:00
  • Lesson 18, Practical Class 12 with Note: Numpy 2
    23:26
  • Lesson 19, Practical Class 13 with Note: Data Cleaning and Processing
    35:59
  • Lesson 20, Practical Class 14- Solution to Class Assignment on Data Cleaning.
    08:28
  • Lesson 21, Practical Quiz 2
  • Lesson 22, Practical Class 15 with Note:- Introduction to WordCloud
    17:27
  • Lesson 23, Practical Class 16:- Data Visualization 1
    22:10
  • Lesson 24, Practical Class 17:- Data Visualization 2
    13:24
  • Lesson 25, Practical Class 18 with Note:- Inferential Statistics 1
    32:17
  • Lesson 26: Quiz on Inferential Statistics
  • Lesson 27, Practical Class 18: Analyze Data at a Glance
    15:30
  • FINAL ASSIGNMENT: CODE SUBMISSION

Student Ratings & Reviews

4.9
Total 9 Ratings
5
8 Ratings
4
1 Rating
3
0 Rating
2
0 Rating
1
0 Rating
PI
Peterclaver Ikechukwu Ugboaja
11 months ago
It was very great and enlightening. I will encourage students to take the course very serious.
God bless BTICL
God bless Dr. Sola.
DJ
David J. Osima
11 months ago
The course was so helpful and innovative
CHIDINMA IHUOMA AMUZIE
CHIDINMA IHUOMA AMUZIE
11 months ago
I enjoyed all the lessons during the Python class. Indeed, it was very insightful, and I can proudly say that I am a beneficiary of BTICL. BTICL to the world.
PY
Philip Yeboah
11 months ago
I just completed the Foundational Course for Python in Data Analytics, and I'm impressed with the comprehensive coverage of key concepts! The course provided a solid foundation in Python programming and its application in data analysis.

*Key Takeaways:*

- Learned the basics of Python programming and data structures
- Understood how to work with popular libraries like Pandas, NumPy.
- Gained hands-on experience with data cleaning, visualization, and analysis

*Strengths:*

- Clear explanations and concise coding examples
- Engaging instructor and interactive learning experience
- Practical assignments and projects helped reinforce learning

I highly recommend this course to anyone looking to start their journey in data analytics with Python. It's an excellent starting point for beginners and provides a strong foundation for further learning.
AE
Adjanor Elvis Jeremiah Martey
12 months ago
I am beginning to love the coding language. It has been a challenging but an interesting course
OO
Ogechukwu Onyebueke
12 months ago
I never new I could come this far in Python. Thanks to Dr Sola and BTICL team for the great work they are doing in impacting such knowledge to people. The mode of teaching is quite commendable. I can't wait to enroll for the advance course.
DA
DAVID AKINTUNDE SONEYE
12 months ago
Before i started data analytics, it felt like i might not go far learning it because people always made it seem like it’s magic. You made it easy for me to learn and made it possible for me to go this far. Thank you, God bless you. Looking forward to SQL and other advance courses.
AA
Abubakar Adekunle Muritala
12 months ago
The course is resourceful!
YO
YUSUF OLANREWAJU AGBABIAKA
12 months ago
I had an incredible experience with it. I have been eager to learn data analysis for some time now especially python, with this opportunity provided by BTICL I was very happy and grateful.